An Improved Fire Detection Algorithm Based on YOLOV9

Fire detection is a critical safety measure across various environments, including residential, commercial, and industrial settings. This project presents an advanced fire and smoke detection algorithm based on YOLOv12, a state-of-the-art deep learning model optimized for real-time object detection. YOLOv12, known for its high precision and speed, is specifically fine-tuned to identify fire and smoke in diverse and dynamic conditions, such as fluctuating lighting and varying smoke densities. The model is trained on a comprehensive dataset consisting of fire and smoke images, allowing it to accurately detect these hazards in real-time, even in complex scenarios. The YOLOv12 model utilizes advanced feature extraction techniques to capture critical patterns associated with fire and smoke, enhancing detection reliability and efficiency. Implemented using Python and TensorFlow, this system aims to provide a robust solution for fire and smoke detection, offering timely alerts to improve safety measures and enable quick responses in emergency situations. The solution is scalable, making it suitable for deployment in various real-world applications, including surveillance systems, industrial monitoring, and building safety systems.

Keywords: Fire Detection, Smoke Detection, YOLOv12, Deep Learning, Real-Time Detection, Object Detection, Fire Safety, Python, TensorFlow, Computer Vision.